文献类型：期刊文献

英文题名：Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

作者：Liu, Qian[1];Liu, Tao[2];Zhang, Weikang[3];Zhang, Fang[4]

机构：[1]Anhui Vocat & Tech Coll, Hefei 230061, Anhui, Peoples R China;[2]Qing Jian Grp Co Ltd, Qingdao 266000, Shandong, Peoples R China;[3]Wanjiang Univ Technol, Maanshan 243000, Anhui, Peoples R China;[4]Shaoxing Univ, Coll Civil Engn, Shaoxing 312000, Zhejiang, Peoples R China

年份：2022

卷号：2022

外文期刊名：SCIENTIFIC PROGRAMMING

收录：SCI-EXPANDED(收录号：WOS:000870001900002)、、EI(收录号：20224312990988)、Scopus(收录号：2-s2.0-85140027794)、WOS

基金：The authors thank the Key Project of Natural Science Research in Anhui Province, Study on Water Restoration Technology of Microbial Filter Bed (No. KJ2016A389).

语种：英文

外文关键词：Computational efficiency - Deep learning - Learning systems

外文摘要：This study proposes the first fully deep learning-based structural response intelligent computing framework for civil engineering. For the first time, from the data side to the model side, the structural information of the structure itself and any loading system is comprehensively considered, which can be applied to materials, components, and even structures, system and other multi-level mechanical response prediction problems. First, according to the characteristics of structural calculation scenarios, a unified data interface mode for structural static characteristics is formulated, which preserves the original structural information input and effectively reduces manual intervention. On this basis, an attention mechanism and a deep cross network are introduced, and a structural static feature representation learning model PADCN is proposed, which can take into account the memory and generalization of structural static features, and mine the coupling relationship of different structural information. Then, the PADCN model is integrated with the dynamic feature prediction model Mechformer and connected with the designed general data interface to form an end-to-end data-driven structural response intelligent computing framework. In order to verify the validity of the framework, numerical experiments were carried out with the steel plate shear wall structure as the carrier, in which a data augmentation algorithm suitable for the field of structural calculation was proposed to alleviate the problem of lack of structural engineering data. The results show that the deep learning model based on this framework successfully predicts the whole-process nonlinear response of specimens with different structures, the simulation accuracy is better than that of the fine finite element model, and the computational efficiency exceeds the traditional numerical method by more than 1000 times, achieving a qualitative improvement. It is proven that the intelligent computing framework has excellent accuracy and efficiency.